Artificial intelligence has become an essential tool for businesses and organizations across industries. However, as the amount of data used to train and execute AI models grows, so do concerns about data privacy and confidentiality. This is evident in the amount of organizations that have banned generative AI on their network after companies, such as Samsung, have seen generative AI models suck up trade secrets and IP. It is essential that AI models are able to work with confidential data while keeping the privacy of that data intact.
Confidentiality and privacy in AI
The importance of confidentiality and privacy in AI cannot be overstated. Many applications of AI, such as medical diagnosis, financial modeling, and national security, require high levels of privacy and confidentiality to ensure the safety and security of individuals and organizations. The use of confidential data in AI models must be done in a way that preserves the privacy of that data, while still enabling the development of accurate and effective models.
Seneca’s approach
Seneca's approach to confidential AI addresses the growing demand for secure AI solutions. Seneca’s Zero Knowledge Environment offers a flexible platform that enables organizations to build and deploy AI models with confidence, knowing that their data is unequivocally secure. By leveraging hardware security features, distributed data management, decentralized computation infrastructure, and private smart contracts, Seneca's paradigm presents a powerful and flexible platform for confidential AI that enables organizations to unlock the full potential of AI while safeguarding the privacy and security of their data.
Seneca's approach to confidential AI represents a powerful new paradigm for AI development and deployment, offering unprecedented levels of security, privacy, and efficiency for organizations and individuals seeking to build and deploy advanced AI applications.
Sources/supporting articles
"AI and Data Privacy: A New Era of Business Compliance" by Forbes
The growing importance of data privacy and compliance in AI applications, and the need for businesses to take steps to ensure that their AI models are developed and deployed in a way that protects the privacy of sensitive data.
"The Need for Confidentiality in Artificial Intelligence" by The Harvard Law School Forum on Corporate Governance This article discusses the importance of confidentiality in AI applications, particularly in areas such as healthcare and finance, and the legal and ethical considerations associated with maintaining data privacy and confidentiality.
“How AI data privacy can help your enterprise” - TechTarget Enterprises benefit in many ways from AI data privacy tools that reduce the need for manual efforts from data professionals. Read on for top use cases for the growing technology.
"AI and privacy: A look at concerns and challenges" by TechTarget The challenges associated with ensuring data privacy and confidentiality in AI applications, including the need for data protection and transparency.
